Field of the Invention
The present invention generally relates to mathematical optimization and, more specifically, to analytics-driven global optimization strategy selection and refinement.
Description of the Related Art
Mathematical optimization includes a wide variety of different techniques for identifying a combination of parameters that meets one or more objectives. The combination of parameters could relate to any particular domain, including engineering problems such as structural design, financial problems such as portfolio optimization, and so forth. Generally, techniques for performing mathematical optimization include applying different algorithms that vary a set of parameters until an objective function is maximized or minimized.
An expert in mathematical optimization typically approaches an optimization problem by identifying the particular problem domain and then selecting an optimization algorithm that is appropriate for that domain. The expert then defines a set of hyperparameters than control the functionality of the optimization algorithm. The selected optimization algorithm may then be executed with the hyperparameters to search for optimal combinations of parameters. Typically, the expert continuously monitors the performance of the algorithm to ensure that converge occurs. However, as is often the case, many optimization algorithms must be manually selected and applied until a successful strategy can be found.
The manual approach described above is a painstaking and error-prone process, even for experts in mathematical optimization. Consequently, this approach is generally inaccessible for non-experts, who lack the domain-specific knowledge to perform any of the manual steps discussed above.
As the foregoing illustrates, what is needed in the art is a more effective approach to performing mathematical optimization.